1 {gt} package

1.1 常用函数

#基础用法
# 挑选mtcars中的4个变量，保留cyl == 6的行，按照wt排序
mycars <- mtcars %>% 
  select(mpg, cyl, disp, wt) %>% 
  filter(cyl == 6) %>%
  arrange(wt)

mycars

#gt()显示的是变量标签label, 不是变量名称
mycars %>% gt()

mpg	cyl	disp	wt
21.0	6	160.0	2.620
19.7	6	145.0	2.770
21.0	6	160.0	2.875
21.4	6	258.0	3.215
19.2	6	167.6	3.440
17.8	6	167.6	3.440
18.1	6	225.0	3.460

1.1.1 cols_label()

#cols_label()修改变量标签, tab_header()修改表格标题
mycars_table <- mycars %>% 
  gt() %>% 
  cols_label(
    mpg = "Miles per Gallon",
    cyl = "Number of Cylinders",
    disp = "Displacement (cu.in.)",
    wt = "Weight (1000 lbs)"
  ) 
mycars_table

Miles per Gallon	Number of Cylinders	Displacement (cu.in.)	Weight (1000 lbs)
21.0	6	160.0	2.620
19.7	6	145.0	2.770
21.0	6	160.0	2.875
21.4	6	258.0	3.215
19.2	6	167.6	3.440
17.8	6	167.6	3.440
18.1	6	225.0	3.460

1.1.2 tab_header()修改表格标题

# tab_header()修改表格标题
mycars_table <- mycars %>% 
  gt() %>% 
  cols_label(
    mpg = "Miles per Gallon",
    cyl = "Number of Cylinders",
    disp = "Displacement (cu.in.)",
    wt = "Weight (1000 lbs)"
  ) %>% 
  tab_header(
    title = "1974 Motor Trend US Magazine Data",
    subtitle = "Cars with 6 Cylinders"
  )
mycars_table

Miles per Gallon	Number of Cylinders	Displacement (cu.in.)	Weight (1000 lbs)
1974 Motor Trend US Magazine Data
Cars with 6 Cylinders
21.0	6	160.0	2.620
19.7	6	145.0	2.770
21.0	6	160.0	2.875
21.4	6	258.0	3.215
19.2	6	167.6	3.440
17.8	6	167.6	3.440
18.1	6	225.0	3.460

1.1.3 tab_options()

mycars_table <- mycars_table %>% 
  tab_options(
    data_row.padding = px(6), # 行高
    heading.align = 'left',  # 标题对齐方式
    column_labels.background.color = 'dodgerblue4', # 列标签背景颜色
    heading.title.font.size = px(26),  # 标题字体大小
    heading.subtitle.font.size = px(14), # 副标题字体大小
    table_body.hlines.width = px(3) # 表格内部横线宽度
  )
mycars_table

Miles per Gallon	Number of Cylinders	Displacement (cu.in.)	Weight (1000 lbs)
1974 Motor Trend US Magazine Data
Cars with 6 Cylinders
21.0	6	160.0	2.620
19.7	6	145.0	2.770
21.0	6	160.0	2.875
21.4	6	258.0	3.215
19.2	6	167.6	3.440
17.8	6	167.6	3.440
18.1	6	225.0	3.460

1.1.4 tab_style()

mycars_table <- mycars_table %>% 
  tab_style(
    style = cell_text(
      color = "dodgerblue4", # 字体颜色
      weight = "bold",  # 字体加粗
      font = google_font("Open Sans") #查找google字体https://fonts.google.com/
      ),
    locations = cells_title(groups = "title") #locations设置样式应用的位置，应用到标题行
    ) %>%
  tab_style(
    style = cell_fill(color = 'grey90'), # 设置单元格背景颜色
    locations = cells_body(rows = seq(1, nrow(mycars), 2)) #locations设置样式应用应用到标题行
  )

mycars_table

Miles per Gallon	Number of Cylinders	Displacement (cu.in.)	Weight (1000 lbs)
1974 Motor Trend US Magazine Data
Cars with 6 Cylinders
21.0	6	160.0	2.620
19.7	6	145.0	2.770
21.0	6	160.0	2.875
21.4	6	258.0	3.215
19.2	6	167.6	3.440
17.8	6	167.6	3.440
18.1	6	225.0	3.460

1.2 创建主题函数

#创建函数的简单例子：求圆的面积的函数
circle_area <- function(r) {
  pi * r^2
}

circle_area(1)

[1] 3.141593

circle_area(2)

[1] 12.56637

#创建一个自定义主题函数
mygt_theme <- function(gt_tbl) {
  gt_tbl %>% 
    tab_options(
    data_row.padding = px(6), # 行高
    heading.align = 'left',  # 标题对齐方式
    column_labels.background.color = 'dodgerblue4', # 列标签背景颜色
    heading.title.font.size = px(26),  # 标题字体大小
    heading.subtitle.font.size = px(14), # 副标题字体大小
    table_body.hlines.width = px(3) # 表格内部横线宽度
  ) %>% 
  tab_style(
    style = cell_text(
      color = "dodgerblue4", # 字体颜色
      weight = "bold",  # 字体加粗
      font = google_font("Open Sans") #查找google字体https://fonts.google.com/
      ),
    locations = cells_title(groups = "title") #locations设置样式应用的位置，应用到标题行
    )
}

1.3 应用主题函数+其他细节设置

#将iris转换为gt对象，然后使用mygt_theme()函数,适用自定义主题
iris_table <- iris %>% 
  head() %>% 
  gt() %>% 
  mygt_theme() %>%  #应用自定义主题函数
  tab_header(
    title = "Fisher's Iris Data",
    subtitle = "ris setosa, versicolor, and virginica"
  ) %>% 
    tab_style(
    style = cell_text(
      color = "dodgerblue4", # 字体颜色
      weight = "bold",  # 字体加粗
      font = google_font("Open Sans") #查找google字体https://fonts.google.com/
      ),
    locations = cells_title(groups = "title") #locations设置样式应用的位置，应用到标题行
    )

iris_table

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
Fisher's Iris Data
ris setosa, versicolor, and virginica
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa

1.4 高级：创建依赖于数据的主题函数

#提取表中的数据框
iris_table$`_data`

#表中数据框的行数
nrow(iris_table$`_data`)

[1] 6

mygt_theme_grey_row <- function(gt_tbl) {
  n_rows <- nrow(gt_tbl$`_data`) # 获取表格行数
  gt_tbl %>% 
    tab_options(
    data_row.padding = px(6), # 行高
    heading.align = 'left',  # 标题对齐方式
    column_labels.background.color = 'dodgerblue4', # 列标签背景颜色
    heading.title.font.size = px(26),  # 标题字体大小
    heading.subtitle.font.size = px(14), # 副标题字体大小
    table_body.hlines.width = px(3) # 表格内部横线宽度
  ) %>% 
  tab_style(
    style = cell_text(
      color = "dodgerblue4", # 字体颜色
      weight = "bold",  # 字体加粗
      font = google_font("Open Sans") #查找google字体https://fonts.google.com/
      ),
    locations = cells_title(groups = "title") #locations设置样式应用的位置，应用到标题行
    ) %>% 
  tab_style(
    style = cell_fill(color = 'grey90'), # 设置单元格背景颜色
    locations = cells_body(rows = seq(1, n_rows, 2)) #n_rows代表数据框的行数
  )
}
iris_table %>% mygt_theme_grey_row()

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
Fisher's Iris Data
ris setosa, versicolor, and virginica
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa

ggplot2::mpg %>% 
  filter(class == "2seater") %>%
  gt() %>% 
  mygt_theme_grey_row()

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
chevrolet	corvette	5.7	1999	8	manual(m6)	r	16	26	p	2seater
chevrolet	corvette	5.7	1999	8	auto(l4)	r	15	23	p	2seater
chevrolet	corvette	6.2	2008	8	manual(m6)	r	16	26	p	2seater
chevrolet	corvette	6.2	2008	8	auto(s6)	r	15	25	p	2seater
chevrolet	corvette	7.0	2008	8	manual(m6)	r	15	24	p	2seater

1.5 常见报错

# table不能导入gt()函数
# table(mtcars$cyl, mtcars$am) %>% 
#   gt()

table(mtcars$cyl, mtcars$am) %>% 
  str()

 'table' int [1:3, 1:2] 3 4 12 8 3 2
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:3] "4" "6" "8"
  ..$ : chr [1:2] "0" "1"

#正确
table(mtcars$cyl, mtcars$am) %>% 
  as.data.frame() %>%
  gt()

Var1	Var2	Freq
4	0	3
6	0	4
8	0	12
4	1	8
6	1	3
8	1	2

mat <- matrix(1:12, nrow = 3, ncol = 4,
              dimnames = list(letters[1:3], LETTERS[1:4]))
# 报错：矩阵不能导入gt()函数
# mat %>% gt()

mat %>% 
  as.data.frame() %>%
  gt()

A	B	C	D
1	4	7	10
2	5	8	11
3	6	9	12

2 {gtsummary} package

2.1 tbl_summary()函数

library(gtsummary)
library(tidyverse)
head(trial)

head(trial) %>% 
  gt::gt()

Chemotherapy Treatment	Age	Marker Level (ng/mL)	T Stage	Grade	Tumor Response	Patient Died	Months to Death/Censor
Drug A	23	0.160	T1	II	0	0	24.00
Drug B	9	1.107	T2	I	1	0	24.00
Drug A	31	0.277	T1	II	0	0	24.00
Drug A	NA	2.067	T3	III	1	1	17.64
Drug A	51	2.767	T4	III	1	1	16.43
Drug B	39	0.613	T4	I	0	1	15.64

str(trial)

tibble [200 × 8] (S3: tbl_df/tbl/data.frame)
 $ trt     : chr [1:200] "Drug A" "Drug B" "Drug A" "Drug A" ...
  ..- attr(*, "label")= chr "Chemotherapy Treatment"
 $ age     : num [1:200] 23 9 31 NA 51 39 37 32 31 34 ...
  ..- attr(*, "label")= chr "Age"
 $ marker  : num [1:200] 0.16 1.107 0.277 2.067 2.767 ...
  ..- attr(*, "label")= chr "Marker Level (ng/mL)"
 $ stage   : Factor w/ 4 levels "T1","T2","T3",..: 1 2 1 3 4 4 1 1 1 3 ...
  ..- attr(*, "label")= chr "T Stage"
 $ grade   : Factor w/ 3 levels "I","II","III": 2 1 2 3 3 1 2 1 2 1 ...
  ..- attr(*, "label")= chr "Grade"
 $ response: int [1:200] 0 1 0 1 1 0 0 0 0 0 ...
  ..- attr(*, "label")= chr "Tumor Response"
 $ death   : int [1:200] 0 0 0 1 1 1 0 1 0 1 ...
  ..- attr(*, "label")= chr "Patient Died"
 $ ttdeath : num [1:200] 24 24 24 17.6 16.4 ...
  ..- attr(*, "label")= chr "Months to Death/Censor"

#创建sm_trial数据框
sm_trial <- 
  trial %>% 
  select(trt, age, grade, response) 

#报告sm_trial数据框的summary
sm_trial %>% 
  select(-trt) %>% 
  tbl_summary()

Characteristic	N = 200¹
Age	47 (38, 57)
Unknown	11
Grade
I	68 (34%)
II	68 (34%)
III	64 (32%)
Tumor Response	61 (32%)
Unknown	7
¹ Median (IQR); n (%)

#按trt分组后，报告summary
sm_trial %>% 
  tbl_summary(by = trt) #指定分组变量

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹
Age	46 (37, 59)	48 (39, 56)
Unknown	7	4
Grade
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response	28 (29%)	33 (34%)
Unknown	3	4
¹ Median (IQR); n (%)

2.1.1 type = 设置变量的summary type

sm_trial %>% 
  tbl_summary(
    by = trt,
    type = age ~ "continuous2")

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹
Age
Median (IQR)	46 (37, 59)	48 (39, 56)
Unknown	7	4
Grade
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response	28 (29%)	33 (34%)
Unknown	3	4
¹ n (%)

sm_trial %>% 
  tbl_summary(
    by = trt,
    type = response ~ "categorical")

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹
Age	46 (37, 59)	48 (39, 56)
Unknown	7	4
Grade
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response
0	67 (71%)	65 (66%)
1	28 (29%)	33 (34%)
Unknown	3	4
¹ Median (IQR); n (%)

2.1.2 statistics = 自定义要报告的统计量

sm_trial %>% 
  tbl_summary(
    by = trt,
    type = age ~ "continuous2",
    statistic = 
      list(age ~ c("{mean} ({sd})", #均值和标准差放在一行
                   "{min}, {max}"), #最小值和最大值放在一行
           response ~ "{n} / {N} ({p}%)"
           )
      )

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹
Age
Mean (SD)	47 (15)	47 (14)
Range	6, 78	9, 83
Unknown	7	4
Grade
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response	28 / 95 (29%)	33 / 98 (34%)
Unknown	3	4
¹ n (%); n / N (%)

trial %>% 
  tbl_summary(
    by = trt,
    statistic = 
      list(all_continuous() ~ "{mean} ({sd})",
           all_categorical() ~ "{n} ({p}%)"
      )
  )

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹
Age	47 (15)	47 (14)
Unknown	7	4
Marker Level (ng/mL)	1.02 (0.89)	0.82 (0.83)
Unknown	6	4
T Stage
T1	28 (29%)	25 (25%)
T2	25 (26%)	29 (28%)
T3	22 (22%)	21 (21%)
T4	23 (23%)	27 (26%)
Grade
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response	28 (29%)	33 (34%)
Unknown	3	4
Patient Died	52 (53%)	60 (59%)
Months to Death/Censor	20.2 (5.0)	19.0 (5.5)
¹ Mean (SD); n (%)

2.1.3 label = 修改标签

sm_trial %>% 
  tbl_summary(
    by = trt,
    type = age ~ "continuous2",
    statistic = 
      list(age ~ c("{mean} ({sd})", 
                   "{min}, {max}"), 
           response ~ "{n} / {N} ({p}%)"
      ),
    label = grade ~ "Pathologic tumor grade" 
  )

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹
Age
Mean (SD)	47 (15)	47 (14)
Range	6, 78	9, 83
Unknown	7	4
Pathologic tumor grade
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response	28 / 95 (29%)	33 / 98 (34%)
Unknown	3	4
¹ n (%); n / N (%)

2.1.4 设置小数位数

sm_trial %>% 
  tbl_summary(
    by = trt,
    type = age ~ "continuous2",
    statistic = 
      list(age ~ c("{mean} ({sd})", 
                   "{min}, {max}"), 
           response ~ "{n} / {N} ({p}%)"
      ),
    label = grade ~ "Pathologic tumor grade",
    digits = age ~ 1
  )

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹
Age
Mean (SD)	47.0 (14.7)	47.4 (14.0)
Range	6.0, 78.0	9.0, 83.0
Unknown	7	4
Pathologic tumor grade
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response	28 / 95 (29%)	33 / 98 (34%)
Unknown	3	4
¹ n (%); n / N (%)

2.2 add_函数

2.2.1 add_overall()函数

sm_trial %>% 
  tbl_summary(
    by = trt
  ) %>% 
  add_overall()

Characteristic	Overall, N = 200¹	Drug A, N = 98¹	Drug B, N = 102¹
Age	47 (38, 57)	46 (37, 59)	48 (39, 56)
Unknown	11	7	4
Grade
I	68 (34%)	35 (36%)	33 (32%)
II	68 (34%)	32 (33%)	36 (35%)
III	64 (32%)	31 (32%)	33 (32%)
Tumor Response	61 (32%)	28 (29%)	33 (34%)
Unknown	7	3	4
¹ Median (IQR); n (%)

2.2.2 add_n()函数

sm_trial %>% 
  tbl_summary(
    by = trt
  ) %>% 
  add_overall() %>% 
  add_n()

Characteristic	N	Overall, N = 200¹	Drug A, N = 98¹	Drug B, N = 102¹
Age	189	47 (38, 57)	46 (37, 59)	48 (39, 56)
Unknown		11	7	4
Grade	200
I		68 (34%)	35 (36%)	33 (32%)
II		68 (34%)	32 (33%)	36 (35%)
III		64 (32%)	31 (32%)	33 (32%)
Tumor Response	193	61 (32%)	28 (29%)	33 (34%)
Unknown		7	3	4
¹ Median (IQR); n (%)

2.2.3 add_stat_label()函数

sm_trial %>% 
  tbl_summary(
    by = trt,
    missing = "no"
  ) %>% 
  add_overall() %>% 
  add_n() %>% 
  add_stat_label(
    label = all_categorical() ~ "No.(%)"
    )

Characteristic	N	Overall, N = 200	Drug A, N = 98	Drug B, N = 102
Age, Median (IQR)	189	47 (38, 57)	46 (37, 59)	48 (39, 56)
Grade, No.(%)	200
I		68 (34%)	35 (36%)	33 (32%)
II		68 (34%)	32 (33%)	36 (35%)
III		64 (32%)	31 (32%)	33 (32%)
Tumor Response, No.(%)	193	61 (32%)	28 (29%)	33 (34%)

2.2.4 add_p()函数

sm_trial %>% 
  tbl_summary(
    by = trt
  ) %>% 
  add_p()

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹	p-value²
Age	46 (37, 59)	48 (39, 56)	0.7
Unknown	7	4
Grade			0.9
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response	28 (29%)	33 (34%)	0.5
Unknown	3	4
¹ Median (IQR); n (%)
² Wilcoxon rank sum test; Pearson’s Chi-squared test

sm_trial %>% 
  tbl_summary(
    by = trt
  ) %>% 
  add_p(age ~ "t.test", 
        test.args = age ~ list(var.equal = TRUE))

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹	p-value²
Age	46 (37, 59)	48 (39, 56)	0.8
Unknown	7	4
Grade			0.9
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response	28 (29%)	33 (34%)	0.5
Unknown	3	4
¹ Median (IQR); n (%)
² Two Sample t-test; Pearson’s Chi-squared test

2.2.5 bold_p()函数

2.2.6 bold_labels()函数

sm_trial %>% 
  tbl_summary(
    by = trt
  ) %>% 
  add_p() %>% 
  bold_p(t = 0.8) %>% #p值小于0.8的加粗，仅作示范用，默认t = 0.05
  bold_labels() %>%
  italicize_levels()

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹	p-value²
Age	46 (37, 59)	48 (39, 56)	0.7
Unknown	7	4
Grade			0.9
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response	28 (29%)	33 (34%)	0.5
Unknown	3	4
¹ Median (IQR); n (%)
² Wilcoxon rank sum test; Pearson’s Chi-squared test

2.2.7 add_difference()函数

trial %>% 
  select(trt, age, marker, response) %>%
  tbl_summary(
    by = trt,
    statistic = list(all_continuous() ~ "{mean} ({sd})",
                     response ~ "{p}"),
    digits = all_continuous() ~ 2,
    missing = "no"
  ) %>%
  add_difference()

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹	Difference²	95% CI^2,3	p-value²
Age	47.01 (14.71)	47.45 (14.01)	-0.44	-4.6, 3.7	0.8
Marker Level (ng/mL)	1.02 (0.89)	0.82 (0.83)	0.20	-0.05, 0.44	0.12
Tumor Response	29	34	-4.2%	-18%, 9.9%	0.6
¹ Mean (SD); %
² Welch Two Sample t-test; Two sample test for equality of proportions
³ CI = Confidence Interval

3 modify_函数

3.1 modify_header()函数

3.2 modify_spanning_header()函数

3.3 modify_footnote()函数

#查看tbl_summary的header_names
sm_trial %>%
  tbl_summary(
    by = trt,
    missing = "no"
  ) %>%
  show_header_names()




Column Name   Column Header       
------------  --------------------
label         **Characteristic**  
stat_1        **Drug A**, N = 98  
stat_2        **Drug B**, N = 102

sm_trial %>%
  tbl_summary(
    by = trt,
    missing = "no"
  ) %>%
  modify_header(
    stat_1 ~ "**Group A**",
    stat_2 ~ "**Group B"
    ) %>%
  modify_spanning_header(
    all_stat_cols() ~ "**Drug**") %>% 
  modify_footnote(
    all_stat_cols() ~ paste("Median (IQR) for continuous; ",
                            "n(%) for categorical")
    )

Characteristic	Drug
Characteristic	Group A¹	**Group B¹
Age	46 (37, 59)	48 (39, 56)
Grade
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Tumor Response	28 (29%)	33 (34%)
¹ Median (IQR) for continuous; n(%) for categorical

4 tbl_cross()

sm_trial %>% 
  tbl_cross(
    row = trt,
    col = response,
    percent = "row", #计算行百分比
    margin = "row" #计算行总数
  )

	Tumor Response
	0	1	Unknown
Chemotherapy Treatment
Drug A	67 (68%)	28 (29%)	3 (3.1%)
Drug B	65 (64%)	33 (32%)	4 (3.9%)
Total	132 (66%)	61 (31%)	7 (3.5%)

sm_trial %>% 
  tbl_cross(
    row = trt,
    col = response,
    percent = "row",
  ) %>%
  add_p(source_note = TRUE) %>%
  bold_labels()

	Tumor Response			Total
	0	1	Unknown	Total
Chemotherapy Treatment
Drug A	67 (68%)	28 (29%)	3 (3.1%)	98 (100%)
Drug B	65 (64%)	33 (32%)	4 (3.9%)	102 (100%)
Total	132 (66%)	61 (31%)	7 (3.5%)	200 (100%)
Fisher’s exact test, p=0.7

5 tbl_continuous()

sm_trial %>%
  tbl_continuous(
    variable = age,
    by = trt,
    include = grade
  )

Characteristic	Drug A, N = 98¹	Drug B, N = 102¹
Grade
I	46 (36, 60)	48 (42, 55)
II	45 (31, 55)	51 (43, 57)
III	52 (42, 60)	45 (36, 52)
¹ Age: Median (IQR)

6 tbl_strata()

sm_trial %>%
  mutate(grade = paste("Grade", grade)) %>%
  tbl_strata(
    strata = grade, #设置分层变量
    ~ tbl_summary(., by = trt, missing = "no") %>% #.占位符，代表pipe进来的数据
      modify_header(all_stat_cols() ~ "**{level}**")
  )

Characteristic	Grade I		Grade II		Grade III
Characteristic	Drug A¹	Drug B¹	Drug A¹	Drug B¹	Drug A¹	Drug B¹
Age	46 (36, 60)	48 (42, 55)	45 (31, 55)	51 (43, 57)	52 (42, 60)	45 (36, 52)
Tumor Response	8 (23%)	13 (41%)	7 (23%)	12 (36%)	13 (43%)	8 (24%)
¹ Median (IQR); n (%)

7 主题函数

7.1 reset_gtsummary_theme() #重置主题

7.2 theme_gtsummary_journal()

7.3 theme_gtsummary_language()

reset_gtsummary_theme() #重置主题
sm_trial %>%
  tbl_summary() %>% 
  modify_caption("Default Theme")

Default Theme
Characteristic	N = 200¹
Chemotherapy Treatment
Drug A	98 (49%)
Drug B	102 (51%)
Age	47 (38, 57)
Unknown	11
Grade
I	68 (34%)
II	68 (34%)
III	64 (32%)
Tumor Response	61 (32%)
Unknown	7
¹ n (%); Median (IQR)

theme_gtsummary_journal(journal = "jama") #设置主题
sm_trial %>%
  tbl_summary() %>% 
  modify_caption("Journal Theme (JAMA)")

Journal Theme (JAMA)
Characteristic	N = 200
Chemotherapy Treatment, n (%)
Drug A	98 (49)
Drug B	102 (51)
Age, Median (IQR)	47 (38 – 57)
Unknown	11
Grade, n (%)
I	68 (34)
II	68 (34)
III	64 (32)
Tumor Response, n (%)	61 (32)
Unknown	7

theme_gtsummary_language(lang = "zh-cn") #简体中文
sm_trial %>%
  tbl_summary() %>% 
  modify_caption("Language Theme (Chinese)")

Language Theme (Chinese)
特征	N = 200
Chemotherapy Treatment, n (%)
Drug A	98 (49)
Drug B	102 (51)
Age, 中位数 (四分位距)	47 (38 – 57)
未知数	11
Grade, n (%)
I	68 (34)
II	68 (34)
III	64 (32)
Tumor Response, n (%)	61 (32)
未知数	7

theme_gtsummary_language(lang = "zh-tw") #繁体中文
sm_trial %>%
  tbl_summary() %>% 
  modify_caption("Language Theme (Chinese Traditional)")

Language Theme (Chinese Traditional)
特色	N = 200
Chemotherapy Treatment, n (%)
Drug A	98 (49)
Drug B	102 (51)
Age, 中位數 (四分位距)	47 (38 – 57)
未知	11
Grade, n (%)
I	68 (34)
II	68 (34)
III	64 (32)
Tumor Response, n (%)	61 (32)
未知	7

reset_gtsummary_theme()

第7课统计量和检验结果的表格化 R代码

顶刊研习社

Li Zongzhang

2023-10-20

1 {gt} package

1.1 常用函数

1.1.1 cols_label()

1.1.2 tab_header()修改表格标题

1.1.3 tab_options()

1.1.4 tab_style()

1.2 创建主题函数

1.3 应用主题函数+其他细节设置

1.4 高级：创建依赖于数据的主题函数

1.5 常见报错

2 {gtsummary} package

2.1 tbl_summary()函数

2.1.1 type = 设置变量的summary type

2.1.2 statistics = 自定义要报告的统计量

2.1.3 label = 修改标签

2.1.4 设置小数位数

2.2 add_函数

2.2.1 add_overall()函数

2.2.2 add_n()函数

2.2.3 add_stat_label()函数

2.2.4 add_p()函数

2.2.5 bold_p()函数

2.2.6 bold_labels()函数

2.2.7 add_difference()函数

3 modify_函数

3.1 modify_header()函数

3.2 modify_spanning_header()函数

3.3 modify_footnote()函数

4 tbl_cross()

5 tbl_continuous()

6 tbl_strata()

7 主题函数

7.1 reset_gtsummary_theme() #重置主题

7.2 theme_gtsummary_journal()

7.3 theme_gtsummary_language()

第7课 统计量和检验结果的表格化 R代码

顶刊研习社

Li Zongzhang

2023-10-20

1 {gt} package

1.1 常用函数

1.1.1 cols_label()

1.1.2 tab_header()修改表格标题

1.1.3 tab_options()

1.1.4 tab_style()

1.2 创建主题函数

1.3 应用主题函数+其他细节设置

1.4 高级：创建依赖于数据的主题函数

1.5 常见报错

2 {gtsummary} package

2.1 tbl_summary()函数

2.1.1 type = 设置变量的summary type

2.1.2 statistics = 自定义要报告的统计量

2.1.3 label = 修改标签

2.1.4 设置小数位数

2.2 add_函数

2.2.1 add_overall()函数

2.2.2 add_n()函数

2.2.3 add_stat_label()函数

2.2.4 add_p()函数

2.2.5 bold_p()函数

2.2.6 bold_labels()函数

2.2.7 add_difference()函数

3 modify_函数

3.1 modify_header()函数

3.2 modify_spanning_header()函数

3.3 modify_footnote()函数

4 tbl_cross()

5 tbl_continuous()

6 tbl_strata()

7 主题函数

7.1 reset_gtsummary_theme() #重置主题

7.2 theme_gtsummary_journal()

7.3 theme_gtsummary_language()

第7课统计量和检验结果的表格化 R代码